| Type of Material: | Thesis |
| Title: | Convolutional Neural Network Architectures with Reduced Complexity for Image Classification |
| Researcher: | Nivea Kesav |
| Guide: | Jibu Kumar, M G |
| Department: | Department of Electronics and Communication |
| Publisher: | Cochin University of Science & Technology, Cochin |
| Place: | Cochin |
| Year: | 2024 |
| Language: | English |
| Subject: | Convolutional Neural Networks | Electronics and Communication | Engineering and Technology | Image Processing and Deep Learning | Machine Learning | Electronic Science | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Electronics and Communication, Cochin University of Science & Technology, Cochin, Cochin; 2024 |
| Fulltext: | Shodhganga |
| 000 | 00000ntm a2200000ua 4500 | |
| 001 | 456245 | |
| 003 | IN-AhILN | |
| 005 | 2024-10-08 17:08:50 | |
| 008 | __ | 241008t2024||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_456245 |
| 040 | __ | |aCUST_682022|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aNivea Kesav|eResearcher |
| 110 | __ | |aDepartment of Electronics and Communication|bCochin University of Science & Technology, Cochin|dCochin|ein|0U-0253 |
| 245 | __ | |aConvolutional Neural Network Architectures with Reduced Complexity for Image Classification |
| 260 | __ | |aCochin|bCochin University of Science & Technology, Cochin|c2024 |
| 300 | __ | |axvii, 266|dDVD |
| 502 | __ | |cDepartment of Electronics and Communication, Cochin University of Science & Technology, Cochin, Cochin|d2024|bPhD |
| 518 | __ | |d2024|oDate of Award |
| 518 | __ | |oDate of Registration|d2019 |
| 520 | __ | |aMachine learning has opened path for significant advancements in various interdisciplinary fields of research where different tasks are accomplished with minimal human effort. It encompasses a wide range of methods for reasoning and drawing conclusions from data. Machine learning is used in many areas, including biomedical analysis, natural language processing, vehicular networks, cognitive networks, and industrial applications. Deep Learning, subset of machine learning is primarily concerned with models having several deep layers where each layer learns the corresponding features. Convolutional Neural Network (CNN) is a class of deep neural networks with multiple deep layers that identifies different patterns from the input images for classification, object detection and segmentation scenarios. Several deep CNN architectures like Alexnet, VGG16, VGG19, GoogleLeNet, Resnet etc. exist in the area of deep learning research which have been used for different image processing applications. These deep designs re |
| 650 | __ | |aElectronic Science|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aConvolutional Neural Networks |
| 653 | __ | |aElectronics and Communication |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aImage Processing and Deep Learning |
| 653 | __ | |aMachine Learning |
| 700 | __ | |eGuide|aJibu Kumar, M G |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/584484|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.